Meirman Syzdykbayev

Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study. Key features of our proposed GEMA architecture include: utilization of widely used smartphones to make GEMA studies practical; alleviation of the burden of activities on participants by designing clients (mobile applications) that are very lightweight and servers that are heavyweight in terms of functionality; utilization of at least one positioning sensor to determine EMA contexts marked with locations; and communication through the Internet. We believe that our proposed GEMA architecture, with the illustrated foundation for GEMA studies in our exemplar study (PMOMS), will help researchers from any field conduct GEMA studies efficiently and effectively.

Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

Of the many features that smart cities offer, safe and comfortable mobility of pedestrians within the built environment is of particular importance. Safe and comfortable mobility requires that the built environments of smart cities be accessible to all pedestrians, mobility abled and mobility impaired, given their various mobility needs and preferences. This, coupled with advanced technologies such as wayfinding applications, pedestrians can get assistance in finding the best pathways at different locations and times. Wayfinding applications comprise two components, a database component containing accessibility data, and appropriate algorithms that can utilize accessibility data to meet the mobility needs and preferences of all individuals. While wayfinding applications that provide accessibility on both permanent (e.g., steps) and temporary (e.g., snow) pathways are becoming available, there is a gap in current solutions. There are two elements in the gap, one is that the accessibility data used for finding accessible pathways for people with disabilities are not compliant to the widely agreed upon and available standards, another is that the accessibility data are not available in free and open platforms so that they can be used by developers to develop personalized wayfinding applications and services. To fill this gap, in this paper, we propose a new extension in CityGML with accessibility data. We demonstrate the benefits of the new extension by testing various route options within a city. These route options clearly show the differences between commonly (shortest and fastest) requested and produced pathways and accessible pathways that are feasible and preferred by people who are mobility impaired, such as wheelchair users.